Reservoir simulation is a process of inferring the behavior of a real reservoir from the performance of a model of that reservoir. Because mass transfer and fluid flow processes in petroleum reservoirs are so complex, reservoir simulations can only be done using computers. Computer programs that perform calculations to simulate reservoirs are called reservoir simulators. The objective of reservoir simulation is to understand the complex chemical, physical, and fluid flow processes occurring in a petroleum reservoir sufficiently well to be able to predict future behavior of a reservoir and to maximize recovery of hydrocarbons. The reservoir simulator can solve reservoir problems that are not solvable in any other way. For example, a reservoir simulator can predict the consequences of reservoir management decisions.
Reservoir simulation typically refers to the hydrodynamics of flow within a reservoir, but in a larger sense it also refers to the total petroleum system which includes the reservoir, the surface facilities, and any interrelated significant activity.
Compositional reservoir simulations are used to simulate recovery processes for which there is a need to know the compositional changes in at least part of the reservoir. For example, compositional simulations can be helpful in studying (1) depletion of a volatile oil or gas condensate reservoir where phase compositions and properties vary significantly with pressure below bubble or dew point pressures, (2) injection of non-equilibrium gas (dry or enriched) into a black-oil reservoir to mobilize oil by vaporization into a more mobile gas phase or by condensation through an outright (single-contact) or dynamic (multiple-contact) miscibility, and (3) injection of CO.sub.2 into an oil reservoir to mobilize oil by miscible displacement and by oil viscosity reduction and oil swelling.
The compositional model describes reservoir hydrocarbon content as a multiple-component mixture. Gas/oil phase properties and equilibrium are calculated from pressure and composition dependent correlations or more typically from a suitable equation of state (EOS). Several EOSs have been developed and are in use today, including for example the Redlich-Kwong EOS and the Peng-Robinson EOS.
Compositional reservoir simulators using an EOS to describe the phase behavior of multi-component fluid mixtures are expensive to use because of the large number of iterative phase equilibrium calculations and large computer storage space required. The number of equations having to be solved in EOS calculations is proportional to the number of components in the fluid. Since a reservoir fluid can contain hundreds of pure components, it is not economically practical to perform compositional simulations in which all reservoir components are used in the calculations. It is therefore desirable to keep the number of components used in describing a fluid mixture to a minimum.
To limit the computational time of compositional reservoir simulations, a common practice is to pseudoize the fluid description. In the pseudoization, the pure compounds are grouped into a number of component groups, termed pseudocomponents. The pseudocomponents are treated as if they were pure components in subsequent reservoir simulations.
It is obvious that the pseudoization can lead to losses in accuracy and flexibility in the equation of state calculations. The accuracy depends both on how the pseudocomponents are developed and the number of pseudocomponents. The number of pseudocomponents used in a study will usually represent a compromise between accuracy and computational cost. Therefore, considerable effort has been made to formulating pseudoization methods in which the fluids can be described as accurately as possible, with as few pseudocomponents as possible.
Many different methods have been proposed for selecting pseudocomponents. The methods include (1) ordering the original components of the fluid with respect to their normal boiling point, and grouping the original components to form pseudocomponents with approximately equal mole fractions, (2) grouping the original components to form pseudocomponents having approximately equal weight fractions, (3) grouping the pure components with similar properties by an iterative scheme in which the distances between the pure components and the pseudocomponents is minimized, (4) selecting pseudocomponents based on molar averaging of the pure component properties, and (5) grouping components using weight-based averaging of the pure component properties.
In these pseudoization methods, the pseudocomponents are formed by "lumping." Each lumped pseudocomponent contains only a few "base" components, and each base component appears in only one pseudocomponent. These methods work reasonably well for performing simulation computations. However, the pseudoization methods do not directly provide an effective way to "delump" the results. Delumping involves converting the computed results expressed in terms of pseudocomponents back to an expression in terms of the original base components. Several approaches to delumping have been proposed, most of which perform supplementary computations after the simulation has been completed. Some involve delumping only the results of interest, while others require performing computations at all gridblocks for all simulation timesteps.
A pseudoization method that is capable of being delumped can be important in modeling fluid flow between two zones having different fluid characteristics. This delumping capability can be particularly useful in estimating fluid properties of surface processing facilities, which often requires a detailed fluid representation. A few (for example, three to eight) pseudocomponents may be adequate for most reservoir computations, while many more pseudocomponents may be needed to adequately represent a processing facility. In modeling a reservoir and a processing facility, the model in effect comprises two fluid representation regions--the reservoir and the processing facilities--each requiring a different level of detail in its fluid representation. Often it would be desirable to divide the reservoir into similar fluid representation regions. In many reservoir models, significant changes and complex behavior occur in only one part of the model. A larger number of pseudocomponents could be used in one zone and a smaller number in another zone where such behavior does not occur. This would require converting any fluid that crosses a boundary between these different types of zones from one representation to another. Pseudoization methods proposed in the past have not been able to effectively estimate fluid behavior and properties as the fluid flows between such zones.
A need exists for an improved method for developing pseudocomponents that can effectively represent a multi-component fluid in a reservoir and a pseudoization method that can effectively transform a fluid as it flows between regions of a reservoir having different fluid representations.